DevOps.com

  • Latest
    • Articles
    • Features
    • Most Read
    • News
    • News Releases
  • Topics
    • AI
    • Continuous Delivery
    • Continuous Testing
    • Cloud
    • Culture
    • DataOps
    • DevSecOps
    • Enterprise DevOps
    • Leadership Suite
    • DevOps Practice
    • ROELBOB
    • DevOps Toolbox
    • IT as Code
  • Videos/Podcasts
    • Techstrong.tv Podcast
    • Techstrong.tv Video Podcast
    • Techstrong.tv - Twitch
    • DevOps Unbound
  • Webinars
    • Upcoming
    • On-Demand Webinars
  • Library
  • Events
    • Upcoming Events
    • On-Demand Events
  • Sponsored Content
  • Related Sites
    • Techstrong Group
    • Container Journal
    • Security Boulevard
    • Techstrong Research
    • DevOps Chat
    • DevOps Dozen
    • DevOps TV
    • Techstrong TV
    • Techstrong.tv Podcast
    • Techstrong.tv Video Podcast
    • Techstrong.tv - Twitch
  • Media Kit
  • About
  • Sponsor
  • AI
  • Cloud
  • Continuous Delivery
  • Continuous Testing
  • DataOps
  • DevSecOps
  • DevOps Onramp
  • Platform Engineering
  • Low-Code/No-Code
  • IT as Code
  • More
    • Application Performance Management/Monitoring
    • Culture
    • Enterprise DevOps
    • ROELBOB

Home » Blogs » Leadership Suite » 3 Steps to Turn a Data Deluge Into Actionable Intelligence

3 Steps to Turn a Data Deluge Into Actionable Intelligence

Avatar photoBy: Mike Shimerda on November 18, 2020 1 Comment

Digital transformation is creating even more data for organizations to manage effectively. Here’s how to find the intelligence needle in the data haystack

Related Posts
  • 3 Steps to Turn a Data Deluge Into Actionable Intelligence
  • Xentaurs Announces a Strategic Partnership with Mesosphere to Build Next Generation Big Data Analytics and DevOps Platforms
  • Enterprises still struggle to leverage IT as a tool to innovate
    Related Categories
  • Blogs
  • Business of DevOps
  • DevOps Practice
  • Leadership Suite
    Related Topics
  • DataOps
  • digital transformation
  • intelligence
Show more
Show less

Data and analytics transform how companies across all industries identify and act on opportunities to gain a competitive advantage. But many organizations are discovering that ever-growing volumes of the former make the latter increasingly difficult. They struggle to turn raw data into a strategic asset that informs decision-making and accelerates growth. Overcoming that struggle requires conceiving and implementing an effective data transformation initiative. Fortunately, there are three steps to implementing sustainable initiatives that any company can follow, no matter its maturity or size.

TechStrong Con 2023Sponsorships Available

But before taking the first step, it is vital to secure the commitment at the board and management levels that the initiatives will be a companywide priority. That is critical to ensuring the allocation of adequate resources and budget dollars at the outset. Failure to secure senior leadership’s enthusiastic support will impede progress if not completely derail the entire project.

You might not think that would be a difficult sell. But research firm KPMG found that the opposite is true when it examined the fundamental role data analytics will play in the financial services sector over the next 10 years:

“(Many CEOs are) responding incrementally and, at best, tactically with better front-end digital services for customers or process improvements in the back-office. But they’re not addressing the more fundamental issue – that their very business model will need to change. And fast.”

If you encounter resistance, here’s the crux of your argument: Your company is a data company. It doesn’t matter if you sell software, cars, clothing, food or any other product or service; the ever-growing volumes of data you’re collecting inform all business decisions. Or at least, they should.

Curt Hopkins, editor in chief for Hewlett Packard Labs, summed up this point perfectly: “Data has become the new gold that backs the value of companies, though it is a gold that companies must locate, mine and refine before spending. Business has shifted from making things to knowing things, so realizing usable data is the order of the day if a company wants to remain profitable.”

Step 1: What Data Do You Have?

Once you have secured the full support of management, you’re ready to take the first step to launch a data transformation initiative: gain an understanding of precisely what you have and where it “lives.”

Take the time to conduct a thorough exploration of all your raw data sources and where those volumes are stored—likely some combination of individual users’ laptops and other devices, on-premises systems and in the cloud (public, private or hybrid).

Next, determine who has access to your data and how. If you decide to work with an analytics services provider, do you have the necessary extraction processes in place to deliver raw data to your partner? If not, you will need to build that pipeline or move the data into a cloud-based analytics database.

Step 2: What Do You Want to Accomplish?

Once you know what you have and where it is across your organization, the next step is to formulate your initiatives based on your objectives. Classifying all datasets under two broad categories—internal use or external use (i.e. commercialization)—will help narrow down the relevant data for a specific campaign.

Embed responsibilities for secure data handling and usage across all parts of the organization—don’t put them only on the technology teams’ collective shoulders. Assume you will need to organize, transform and enrich data to realize its full value. Remove any barriers to sharing data under NDA and considering a nuanced build-versus-buy approach. Finally, ensure the initiatives map to management by objectives (MBO) to maintain a focus on using data to help the company achieve its business goals. At this point, you’ll need to answer the build-or-buy question to determine if you have these transformation capabilities internally or need to find them externally.

Step 3: How Will You Track Progress and Evaluate Outcomes?

After executing the data initiative, you should regularly measure its outcomes. Hold your teams accountable by setting time-based goals and tracking progress versus plans. Anticipate the need to iterate because harnessing data is a fluid process. Create and maintain a dictionary to keep employees and teams aligned as they progress on their tasks and projects.

One additional and no less critical consideration is to ensure you strike a balance between gaining the ability to use data assets with protecting them from security threats, both cyber and physical. According to  “The Cost of Insider Threats Global Report 2020” by IBM and the Ponemon Institute, the average cost to recover from a breach is between $3 million and $4 million, and that does not account for the loss of customer trust and brand loyalty. Protecting customer and employee personally identifiable information (PII) and all other sensitive data types and complying with all data rights agreements is paramount.

Fifteen years ago, British mathematician Clive Humby famously proclaimed, “data is the new oil,” and the analogy holds up today. Like the raw crude that comes out of the ground, raw data only represents potential value. Just as oil has to be refined into fuel to become useful, data must be analyzed and refined to become functional.

By understanding your supply of raw data, formulating your initiatives and executing and measuring those initiatives, you will be better able to identify opportunities and make more informed decisions that give your organization a competitive advantage.

Filed Under: Blogs, Business of DevOps, DevOps Practice, Leadership Suite Tagged With: DataOps, digital transformation, intelligence

« The Struggles of Working From Home
How to Make a Self-Healing IT Infrastructure a Reality »

Techstrong TV – Live

Click full-screen to enable volume control
Watch latest episodes and shows

Upcoming Webinars

Evolution of Transactional Databases
Monday, January 30, 2023 - 3:00 pm EST
Moving Beyond SBOMs to Secure the Software Supply Chain
Tuesday, January 31, 2023 - 11:00 am EST
Achieving Complete Visibility in IT Operations, Analytics, and Security
Wednesday, February 1, 2023 - 11:00 am EST

Sponsored Content

The Google Cloud DevOps Awards: Apply Now!

January 10, 2023 | Brenna Washington

Codenotary Extends Dynamic SBOM Reach to Serverless Computing Platforms

December 9, 2022 | Mike Vizard

Why a Low-Code Platform Should Have Pro-Code Capabilities

March 24, 2021 | Andrew Manby

AWS Well-Architected Framework Elevates Agility

December 17, 2020 | JT Giri

Practical Approaches to Long-Term Cloud-Native Security

December 5, 2019 | Chris Tozzi

Latest from DevOps.com

Stream Big, Think Bigger: Analyze Streaming Data at Scale
January 27, 2023 | Julia Brouillette
What’s Ahead for the Future of Data Streaming?
January 27, 2023 | Danica Fine
The Strategic Product Backlog: Lead, Follow, Watch and Explore
January 26, 2023 | Chad Sands
Atlassian Extends Automation Framework’s Reach
January 26, 2023 | Mike Vizard
Software Supply Chain Security Debt is Increasing: Here’s How To Pay It Off
January 26, 2023 | Bill Doerrfeld

TSTV Podcast

On-Demand Webinars

DevOps.com Webinar ReplaysDevOps.com Webinar Replays

GET THE TOP STORIES OF THE WEEK

Most Read on DevOps.com

What DevOps Needs to Know About ChatGPT
January 24, 2023 | John Willis
Microsoft Outage Outrage: Was it BGP or DNS?
January 25, 2023 | Richi Jennings
Optimizing Cloud Costs for DevOps With AI-Assisted Orchestra...
January 24, 2023 | Marc Hornbeek
Five Great DevOps Job Opportunities
January 23, 2023 | Mike Vizard
Dynatrace Survey Surfaces State of DevOps in the Enterprise
January 24, 2023 | Mike Vizard
  • Home
  • About DevOps.com
  • Meet our Authors
  • Write for DevOps.com
  • Media Kit
  • Sponsor Info
  • Copyright
  • TOS
  • Privacy Policy

Powered by Techstrong Group, Inc.

© 2023 ·Techstrong Group, Inc.All rights reserved.